Conditional Mean Reversion of Financial Ratios and the Predictability of Returns

42 Pages Posted: 11 Dec 2021

See all articles by Christophe Boucher

Christophe Boucher

CNRS-Université Paris Nanterre

Alexandre Jasinski

CNRS-Université Paris Nanterre

Sessi Tokpavi

University of Orleans

Abstract

While traditional predictive regressions for stock returns using financial ratios are empirically proven to be valuable at long-term horizons, evidence of predictability at few-month horizons is still weak. In this paper, based on the empirical regularity of a typical dynamic of stock returns following the occurrence of a mean reversion in the US Shiller CAPE ratio when the latter is high, we propose a new predictive regression model that exploits this stylized fact. In-sample regressions approximating the occurrence of mean reversion by the smoothed probability from a regime-switching model show superior predictive powers of the new specification at few-month horizons. These results also hold out-of-sample, exploiting the link between the term spread as business cycle variable and the occurrence of mean reversion in the US Shiller CAPE ratio. For instance, the out-of-sample R-squared of the new predictive regression model is ten (four) times bigger than that of the traditional predictive model at a 6 (12) month horizon. Our results are robust with respect to the choice of the valuation ratio (CAPE, excess CAPE or dividend yield), and countries (Canada, France, Germany and the UK). We also conduct a mean-variance asset allocation exercise which confirms the superiority of the new predictive regression in terms of utility gain.

Keywords: G12, G17, C53, E32

Suggested Citation

Boucher, Christophe and Jasinski, Alexandre and Tokpavi, Sessi, Conditional Mean Reversion of Financial Ratios and the Predictability of Returns. Available at SSRN: https://ssrn.com/abstract=3983094 or http://dx.doi.org/10.2139/ssrn.3983094

Christophe Boucher

CNRS-Université Paris Nanterre ( email )

Alexandre Jasinski

CNRS-Université Paris Nanterre ( email )

France

Sessi Tokpavi (Contact Author)

University of Orleans ( email )

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